Anesthesia Clinical Workload Estimated From Electronic Health Record Documentation vs Billed Relative Value Units

Abstract

Importance: Accurate measurements of clinical workload are needed to inform health care policy. Existing methods for measuring clinical workload rely on surveys or time-motion studies, which are labor-intensive to collect and subject to biases.

Objective: To compare anesthesia clinical workload estimated from electronic health record (EHR) audit log data vs billed relative value units.

Design, setting, and participants: This cross-sectional study of anesthetic encounters occurring between August 26, 2019, and February 9, 2020, used data from 8 academic hospitals, community hospitals, and surgical centers across Missouri and Illinois. Clinicians who provided anesthetic services for at least 1 surgical encounter were included. Data were analyzed from January 2022 to January 2023.

Exposure: Anesthetic encounters associated with a surgical procedure were included. Encounters associated with labor analgesia and endoscopy were excluded.

Main outcomes and measures: For each encounter, EHR-derived clinical workload was estimated as the sum of all EHR actions recorded in the audit log by anesthesia clinicians who provided care. Billing-derived clinical workload was measured as the total number of units billed for the encounter. A linear mixed-effects model was used to estimate the relative contribution of patient complexity (American Society of Anesthesiology [ASA] physical status modifier), procedure complexity (ASA base unit value for the procedure), and anesthetic duration (time units) to EHR-derived and billing-derived workload. The resulting β coefficients were interpreted as the expected effect of a 1-unit change in each independent variable on the standardized workload outcome. The analysis plan was developed after the data were obtained.

Results: A total of 405 clinicians who provided anesthesia for 31 688 encounters were included in the study. A total of 8 288 132 audit log actions corresponding to 39 131 hours of EHR use were used to measure EHR-derived workload. The contributions of patient complexity, procedural complexity, and anesthesia duration to EHR-derived workload differed significantly from their contributions to billing-derived workload. The contribution of patient complexity toward EHR-derived workload (β = 0.162; 95% CI, 0.153-0.171) was more than 50% greater than its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.097-0.116; P < .001). In contrast, the contribution of procedure complexity toward EHR-derived workload (β = 0.033; 95% CI, 0.031-0.035) was approximately one-third its contribution toward billing-derived workload (β = 0.106; 95% CI, 0.104-0.108; P < .001).

Conclusions and relevance: In this cross-sectional study of 8 hospitals, reimbursement for anesthesiology services overcompensated for procedural complexity and undercompensated for patient complexity. This method for measuring clinical workload could be used to improve reimbursement valuations for anesthesia and other specialties.

Figure 1.. Distribution of Electronic Health Record (EHR) Activity by Phase of Care Across All Anesthetic Encounters

A, Kernel density histogram illustrating the number of EHR actions observed in each phase of care for all anesthetic encounters included in the study. The y-axis is shown in arbitrary units, indicating the density of observations in each bin. B, Kernel density histogram illustrating the density of EHR actions in the intraoperative period relative to the start and end of the anesthetic. Time on the x-axis is normalized such that 0 is the start of the anesthetic and 1 corresponds to the end of the anesthetic.

Figure 2.. Association Between Electronic Health Record (EHR)—Derived Workload and Patient Complexity, Procedure Complexity, and Anesthesia Duration

Kernel density histograms illustrating the distribution of the total number of EHR actions observed for the encounter, (A) stratified by the patient’s American Society of Anesthesiology (ASA) physical status score as a measure of patient complexity, (B) stratified by the duration of the anesthetic in hours, and (C) stratified by the procedure’s assigned base unit value according to the ASA Relative Value Guide as a measure of procedure complexity.

Figure 3.. Correlation Between Electronic Health Record (EHR)—Derived Workload and Billing-Derived Workload

A, For each anesthesia Current Procedural Terminology code, the mean number of EHR actions observed and mean number of units billed was plotted, with a single dot for each procedure code, colored by that procedure code’s base unit value. Only anesthesia Current Procedural Terminology codes with at least 50 encounters are shown; this included 29 747 of 31 688 encounters (94%). See eTable 2 in Supplement 1 for a list of all anesthesia Current Procedural Terminology codes included. B, For each encounter, the number of EHR actions observed and number of units billed were plotted. Due to the large number of encounters, a 2-dimensional histogram is shown, with color indicating the number of encounters in each bin.

Figure 4.. Relative Contribution of Anesthesia Duration, Patient Complexity, and Procedure Complexity to Electronic Health Record (EHR)—Derived and Billing-Derived Workload

A linear mixed-effects model was used to measure the contribution of patient complexity, procedure complexity, and anesthesia duration to the 2 outcome variables: standardized EHR-derived workload and standardized billing-derived workload. The effect estimates refer to the contribution of a 1-unit change in each independent variable toward each standardized outcome variable. Because billed units are defined as the sum of time units, the American Society of Anesthesiology modifier, and procedure base units, the coefficients fit for these independent variables towards the billed unit outcome were expected to be equal. The primary goal of this analysis was to examine the relative value of the estimate for the EHR-derived outcome compared with the billing-derived outcome, which was interpreted as relative difference in contribution of each independent variable toward each outcome.